CAJ | 학술논문

半监督聚类方法利用少量标记数据提高聚类算法的性能,已逐渐发展成为模式识别及相关领域的研究热点.文中首先综述了半监督聚类算法的一些新进展,包括基于约束的方法、基于距离的方法和基于距离与约束的融合方法.然后提出一种基于约束的半监督模糊C-means聚类算法.实验表明,该算法与传统的模糊C-means及半监督K-means方法相比,具有更好的聚类精度.
반감독취류방법이용소량표기수거제고취류산법적성능,이축점발전성위모식식별급상관영역적연구열점.문중수선종술료반감독취류산법적일사신진전,포괄기우약속적방법、기우거리적방법화기우거리여약속적융합방법.연후제출일충기우약속적반감독모호C-means취류산법.실험표명,해산법여전통적모호C-means급반감독K-means방법상비,구유경호적취류정도.
Small amount of labeled data are used in semi-supervised clustering algorithms to improve the performance of the algorithms. It is a research hotspot in pattern recognition and its related fields. In this paper, some developments on semi-supervised clustering are introduced including constraint-based, distance-based and the combination of them. Using semi-supervised strategy to fuzzy C-means, a semi-supervised fuzzy C-means (constrained FCM) algorithm is proposed. Experimental results show that the proposed method obtains better accuracy compared with FCM and semi-supervised K-means.